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University of Oxford

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PhD in Real-World Data Transformation Fidelity, OMOP Data Standardisation, and Graph Theory University of Oxford in United Kingdom

Degree Level

PhD

Field of study

Computer Science

Funding

Competition-funded PhD project. The post is described as a funded PhD project; the page also notes limited financial support for conference travel and that the DPhil/MSc by research will commence in October 2027.

Deadline

Dec 1, 2026

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Country

United Kingdom

University

University of Oxford

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Keywords

Computer Science
Epidemiology
Biology
Mathematics
Medical Statistics
Graph Theory
Medical Science
Data Standards
Real-world Data
Data Harmonization
Statistics

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About this position

University of Oxford is advertising a funded PhD/DPhil project at the Botnar Research Centre on real-world data transformation fidelity, OMOP data standardisation, and graph theory.

The project asks how well source healthcare data are transformed into Common Data Models, especially the OMOP CDM, and aims to develop novel measures of structural similarity and distance between original real-world data and transformed datasets. The research will also build tools to detect missing, incorrect, incomplete, or redundant transformations, and create algorithmic models to help improve and automate ETL mapping corrections.

This is a 3-year DPhil project between the Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences (NDORMS) and the Department of Computer Science at the University of Oxford. The supervisory team is Dr Antonella Delmestri, Associate Professor Sandra Kiefer, and Dr Marta Pineda-Moncusí.

Relevant academic areas include data analysis, data science, epidemiology, medical statistics, biological sciences, computer science, and medicine. The team highlights expertise in health data sciences, machine learning, epidemiology, pharmacoepidemiology, pharmacogenomics, graph theory, verification, automata theory, and computational biomedicine.

Applicants should have, or expect to obtain, a first or upper second-class BSc degree or equivalent in a relevant subject, and must provide evidence of English language competence where applicable. The project includes hands-on training in real-world data, medical records, genetic data, applied AI, and real-world evidence, plus access to Oxford training programmes and some financial support for conference travel.

Applications are made through the University of Oxford graduate admissions process using course code RD_NNRA1. Interested applicants should contact the supervisors to register interest and may email [email protected] for advice. Applications open mid-September, and the deadline is 1 December 2026 at 12:00. The project is scheduled to commence in October 2027.

Funding details

Competition-funded PhD project. The post is described as a funded PhD project; the page also notes limited financial support for conference travel and that the DPhil/MSc by research will commence in October 2027.

What's required

Applicants should have, or expect to obtain, a first or upper second-class BSc degree or equivalent in a relevant subject. English language competence is required where applicable. The project is suitable for candidates with interest in real-world data, health data sciences, data standardisation, data harmonisation, OMOP CDM, graph theory, and computational methods.

How to apply

Contact the relevant supervisor(s) to register your interest and, if needed, email the departmental Education Team at [email protected] for guidance. Submit an official application through the University of Oxford graduate admissions route using the specified course code RD_NNRA1. Applications open mid-September and the deadline is 12:00 on 1 December.

More information can be found here

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